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Based on available toxicological data and matrix of structural descriptors 7-ethoxyresorufin-O-diethylase (EROD) and luciferase (luc) inducing potency for 75 congeners of chloronaphthalene was predicted using quantitative structure– activity relationships (QSAR) strategy. The most active congeners in EROD and luciferase bioassays were CN congeners nos. 75 and 67. Some empirical rules describing toxic PCNs were formulated. [ABSTRACT FROM AUTHOR]

Presents a study which determined the thermodynamic and physico-chemical descriptors of polychlorinated naphthalenes to explain their environmental behavior and toxic effects. Methodology; Results; Discussion.

63 congeners of chloronaphthalene represented by 53 peaks fractionated and separated using two-dimensional HPLC and DB-17 capillary column were quantified using HRMS in ten samples of pine needles collected in 1999 around Tokyo Bay in Japan. Similarities and differences of chloronaphthalene concentrations and patterns between 10 sampling sites were studied using multivariate analysis. Total PCN concentrations ranged from 250 to 2100 pg/g wet weight. Except for one site, tri- and tetra-CNs highly dominated (from 54 to 80%) in CN homologue patterns of pine needles. At the easternmost site near the town of Tateyama in Chiba Prefecture the contribution from octaCN was ∼20%, while that of tri- and tetra-CNs ∼42%. Pine needles sampled from the sites around the innermost part of Tokyo Bay showed the highest load of PCNs. A multivariate analysis using the three most significant principal components explained 91% of the total variance in the measurement data. The greatest positive loading to PC1 is from the CN congeners nos. 13, 14/21/24, 15, 16, 17, 18, 19, 20, 22/23, 25, 26, 27, 28/36, 29, 30/32, 31, 33/34/37, 35, 40, 42, 43/45, 44, 47, 49, 50, 51, 52/60, 53, 57, 58, 59, 61, 62, 64, 65, 66/67, 68, 69, 71 and 72, and explains 65% variance in the data set. For PC2 the largest positive loading is from CNs nos. 74 and 75, and negative load from CN nos. 38, 41, 46 and 48, which explains 17% of the variance. In case of PC3 the largest negative load is from CNs nos. 54, 56, 63, 70 and 73. A profile of the combustion process related CN congeners measured such as nos. 44, 48 and 54 didn't show any specific trend implying pollution from diffused sources of various types. [ABSTRACT FROM AUTHOR]

The objective of the subsequent study was to examine the probability of PFAS (per- and polyfluorinated alkyl substances) binding to various NHRs (nuclear hormone receptors) and to identify their structural features that contribute most to the binding score (BS). We evaluated the BS for PFAS in relation to 7 selected NHRs - 4 with additional antagonist forms (Retinoid X receptor alpha - RXRα, Liver X receptor alpha - LXRα, Liver X receptor beta - LXRβ, Estrogen receptor alpha - ERα, Estrogen receptor alpha antagonist - anti-ERα, Estrogen receptor beta - ERβ, Estrogen receptor beta antagonist - anti-ERβ, Glucocorticoid receptor - GR, Glucocorticoid receptor antagonist - anti-GR, Androgen receptor - AR, Androgen receptor antagonist - anti-AR). We based our study on the results of molecular docking, which we used to develop MLR-QSAR (Multiple Linear Regression - Quantitative Structure-Activity Relationship) models. The models we developed allowed us to predict the BS for an extensive set of PFAS compounds from the NORMAN database (more than 4000) - virtual screening. The probability of PFAS binding to selected receptors was determined by structural features such as particle size, branching, and fluorine content. These variables were also identified in the literature reports of experimental studies as the most important for this group of compounds. The research focused on receptors from the terpenoid group. The RXRα, LXRα and β, GR, and anti-GR receptors were shown to be the group less likely to be affected by PFAS. Sex hormones such as AR, anti-AR, ERα and ERβ with their antagonist forms are the most affected., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Ltd.)

A key step in building regulatory acceptance of alternative or non-animal test methods has long been the use of interlaboratory comparisons or round-robins (RRs), in which a common test material and standard operating procedure is provided to all participants, who measure the specific endpoint and return their data for statistical comparison to demonstrate the reproducibility of the method. While there is currently no standard approach for the comparison of modelling approaches, consensus modelling is emerging as a "modelling equivalent" of a RR. We demonstrate here a novel approach to evaluate the performance of different models for the same endpoint (nanomaterials' zeta potential) trained using a common dataset, through generation of a consensus model, leading to increased confidence in the model predictions and underlying models. Using a publicly available dataset, four research groups (NovaMechanics Ltd. (NovaM)-Cyprus, National Technical University of Athens (NTUA)-Greece, QSAR Lab Ltd.-Poland, and DTC Lab-India) built five distinct machine learning (ML) models for the in silico prediction of the zeta potential of metal and metal oxide-nanomaterials (NMs) in aqueous media. The individual models were integrated into a consensus modelling scheme, enhancing their predictive accuracy and reducing their biases. The consensus models outperform the individual models, resulting in more reliable predictions. We propose this approach as a valuable method for increasing the validity of nanoinformatics models and driving regulatory acceptance of in silico new approach methodologies for the use within an "Integrated Approach to Testing and Assessment" (IATA) for risk assessment of NMs., (Copyright © 2024, Varsou et al.)

The pioneering work on liposomes in the 1960s and subsequent research in controlled drug release systems significantly advances the development of nanocarriers (NCs) for drug delivery. This field is evolved to include a diverse array of nanocarriers such as liposomes, polymeric nanoparticles, dendrimers, and more, each tailored to specific therapeutic applications. Despite significant achievements, the clinical translation of nanocarriers is limited, primarily due to the low efficiency of drug delivery and an incomplete understanding of nanocarrier interactions with biological systems. Addressing these challenges requires interdisciplinary collaboration and a deep understanding of the nano-bio interface. To enhance nanocarrier design, scientists employ both physics-based and data-driven models. Physics-based models provide detailed insights into chemical reactions and interactions at atomic and molecular scales, while data-driven models leverage machine learning to analyze large datasets and uncover hidden mechanisms. The integration of these models presents challenges such as harmonizing different modeling approaches and ensuring model validation and generalization across biological systems. However, this integration is crucial for developing effective and targeted nanocarrier systems. By integrating these approaches with enhanced data infrastructure, explainable AI, computational advances, and machine learning potentials, researchers can develop innovative nanomedicine solutions, ultimately improving therapeutic outcomes., (© 2024 Wiley‐VCH GmbH.)

The bioconcentration factor (BCF) is an important parameter that gives information regarding the ability of a contaminant to be taken up by organisms from the water. Per- and polyfluoroalkyl substances (PFAS) are widespread in the environment, causing concern regarding their impact on human health. Due to the lack of available bioaccumulation data for most compounds in the PFAS group, we developed a quantitative structure-property relationship (QSPR) model to predict the log BCF for fish (taxonomic class Teleostei), based on experimental data available for the most studied 33 representatives of this group of compounds. Furthermore, we implemented the developed model to predict log BCF for an external dataset of 2209 PFAS. Consequently, 1045 PFAS were found not to be bioaccumulative, 208 were classified as bioaccumulative, and 956 were predicted to be very bioaccumulative. Finally, we obtained the high correlation (R 2  = 0.844) between the log BCFs obtained in laboratory and field studies for 13 PFAS. In silico analyses indicate that PFAS bioconcentration depends on the size (chain length - number of CF 2 groups in alkyl tail/chain) of a molecule, as well as on the atomic distribution properties. In general, long-chain PFAS - above 8 and 6 carbon atoms for perfluorinated carboxylic acids (PFCAs)and perfluorinated sulfonic acids (PFSAs), respectively - tend to bioconcentrate more compared to the short-chain ones. In conclusion, predicting BCF on fish is possible for a wide range of fluorinated compounds, which can be further used for estimating PFAS behavior in the environment., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Ltd.)

Making research data findable, accessible, interoperable and reusable (FAIR) is typically hampered by a lack of skills in technical aspects of data management by data generators and a lack of resources. We developed a Template Wizard for researchers to easily create templates suitable for consistently capturing data and metadata from their experiments. The templates are easy to use and enable the compilation of machine-readable metadata to accompany data generation and align them to existing community standards and databases, such as eNanoMapper, streamlining the adoption of the FAIR principles. These templates are citable objects and are available as online tools. The Template Wizard is designed to be user friendly and facilitates using and reusing existing templates for new projects or project extensions. The wizard is accompanied by an online template validator, which allows self-evaluation of the template (to ensure mapping to the data schema and machine readability of the captured data) and transformation by an open-source parser into machine-readable formats, compliant with the FAIR principles. The templates are based on extensive collective experience in nanosafety data collection and include over 60 harmonized data entry templates for physicochemical characterization and hazard assessment (cell viability, genotoxicity, environmental organism dose-response tests, omics), as well as exposure and release studies. The templates are generalizable across fields and have already been extended and adapted for microplastics and advanced materials research. The harmonized templates improve the reliability of interlaboratory comparisons, data reuse and meta-analyses and can facilitate the safety evaluation and regulation process for (nano) materials., (© 2024. Springer Nature Limited.)

Bisphenol A alternatives are manufactured as potentially less harmful substitutes of bisphenol A (BPA) that offer similar functionality. These alternatives are already in the market, entering the environment and thus raising ecological concerns. However, it can be expected that levels of BPA alternatives will dominate in the future, they are limited information on their environmental safety. The EU PARC project highlights BPA alternatives as priority chemicals and consolidates information on BPA alternatives, with a focus on environmental relevance and on the identification of the research gaps. The review highlighted aspects and future perspectives. In brief, an extension of environmental monitoring is crucial, extending it to cover BPA alternatives to track their levels and facilitate the timely implementation of mitigation measures. The biological activity has been studied for BPA alternatives, but in a non-systematic way and prioritized a limited number of chemicals. For several BPA alternatives, the data has already provided substantial evidence regarding their potential harm to the environment. We stress the importance of conducting more comprehensive assessments that go beyond the traditional reproductive studies and focus on overlooked relevant endpoints. Future research should also consider mixture effects, realistic environmental concentrations, and the long-term consequences on biota and ecosystems., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Water pollution has become a critical global concern requiring effective monitoring techniques and robust protection strategies. Contaminants of emerging concern (CECs) are increasingly detected in various water sources, with their harmful effects on humans and ecosystems continually evolving. Based on literature reports highlighting the promising sorption properties of metal-organic frameworks (MOFs), the aim of this study was to evaluate the suitability of NH 2 -MIL-125 (Ti) and UiO-66 (Ce) as sorbents in passive sampling devices (MOFs-PSDs) for the collection and extraction of a wide group of CECs. Solvothermal methods were used to synthesize MOFs, and the characterization of the obtained materials was performed using field-emission scanning electron microscopy (FE-SEM), powder X-ray diffractometry (pXRD) and Fourier-transform infrared (FTIR) spectroscopy. The research demonstrated the sorption capabilities of the tested MOFs, the ease and rapidity of their chemical regeneration and the possibility of reuse as sorbents. Using chemometric analysis, the structural properties of CECs determining the sorption efficiency on the surface of NH 2 -MIL-125 (Ti) were identified. The MOFs-PSDs were lab-calibrated to examine the kinetics of analytes sorption and determine the sampling rates (R s ). MOFs-PSDs and CNTs-PSDs (PSDs containing carbon nanotubes as a sorbent) were then placed in the Elbląg River and the Vistula Lagoon to sampling and extraction of the target compounds from the water. CNTs-PSDs were selected, based on our previous research, for the comparison of the effectiveness of the MOFs-PSDs in environmental monitoring. MOFs-PSDs were successfully used in monitoring of CECs in water. The time-weighted average concentrations (C TWA ) of 2-hydroxycarbamazepine, carbamazepine-10,11-epoxide, p-nitrophenol, 3,5-dichlorophenol and caffeine were determined in the Elbląg River and C TWA of metoprolol, diclofenac, 2-hydroxycarbamazepine, carbamazepine-10,11-epoxide, p-nitrophenol, 3,5-dichlorophenol and caffeine were determine in the Vistula Lagoon using MOFs-PSDs and a high-performance liquid chromatography coupled with triple quadrupole mass spectrometer., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Numerous processes such as solubility, agglomeration/aggregation, or protein corona formation may change over time and significantly affect engineered nanomaterial (ENM) structure, property, and availability, resulting in their reduced or increased toxicological activity. Therefore, understanding the dynamics of these processes is essential for assessing and managing the risks of ENMs during their lifecycle, ensuring safety by design. Of these processes, the importance of solubility (i.e., the ability to release ions from the surface) is undeniable. Thus, we propose a practical approach, the Kalapus equation (KEq), to determine ENMs' dissolution over time. As a proof-of-concept , the KEq was applied to determine the solubility of six commercially used metal and metal oxide nanoparticles over time. The KEq exhibited a higher coefficient of determination (R 2 = 0.995-0.999) than the logarithmic equation (R 2 = 0.835-0.986), and the pseudo-first-order equation (R 2 = 0.915-0.994) over a range of experimental data. The newly introduced Kalapus equation outperformed the logarithmic and pseudo-first-order equations when extrapolating beyond the time range in which solubility was experimentally determined. The mean absolute error in solubility prediction for the KEq was 3.29 % and 4.28 % for the first and second data points, respectively, significantly lower than the 13.46 % and 18.05 % observed for the pseudo-first-order/first-order equation. The proposed equation can be used as a part of New Generation Risk Assessment (NGRA) methodology, especially new Integrated Approaches to Testing and Assessments (IATAs)., Competing Interests: The authors have no conflicts of interest to declare. All co-authors have seen and agree with the contents of the manuscript and there is no financial interest to report. We certify that the submission is original work and is not under review at any other publication., (© 2024 Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology.)

Viruses are a group of widespread organisms that are often responsible for very dangerous diseases, as most of them follow a mechanism to multiply and infect their hosts as quickly as possible. Pathogen viruses also mutate regularly, with the result that measures to prevent virus transmission and recover from the disease caused are often limited. The development of new substances is very time-consuming and highly budgeted and requires the sacrifice of many living organisms. Computational chemistry methods allow faster analysis at a much lower cost and, most importantly, reduce the number of living organisms sacrificed experimentally to a minimum. Ionic liquids (ILs) are a group of chemical compounds that could potentially find a wide range of applications due to their potential virucidal activity. In our study, we conducted a complex computational analysis to predict the antiviral activity of ionic liquids against three surrogate viruses: two nonenveloped viruses, Listeria monocytogenes phage P100 and Escherichia coli phage MS2, and one enveloped virus, Pseudomonas syringae phage Phi6. Based on experimental data of toxic activity (logEC 90 ), we assigned activity classes to 154 ILs. Prediction models were created and validated according to the Organization for Economic Co-operation and Development (OECD) recommendations using the Classification Tree method. Further, we performed an external validation of our models through virtual screening on a set of 1277 theoretically generated ionic liquids and then selected 10 active ionic liquids, which were synthesized to verify their activity against the analyzed viruses. Our study proved the effectiveness and efficiency of computational methods to predict the antiviral activity of ionic liquids. Thus, computational models are a cost-effective alternative approach compared with time-consuming experimental studies where live animals are involved.

Per- and polyfluorinated alkyl substances (PFAS), known for their widespread environmental presence and slow degradation, pose significant concerns. Of the approximately 10,000 known PFAS, only a few have undergone comprehensive testing, resulting in limited experimental data. In this study, we employed a combination of physics-based methods and data-driven models to address gaps in PFAS bioaccumulation potential. Using the COnductor-like Screening MOdel for Realistic Solvents (COSMO-RS) method, we predicted n-octanol/water partition coefficients (logK OW ), crucial for PFAS bioaccumulation. Our developed Quantitative Structure-Property Relationship (QSPR) model exhibited high accuracy (R 2  = 0.95, RMSE C  = 0.75) and strong predictive ability (Q 2 LOO  = 0.93, RMSE CV  = 0.83). Leveraging the extensive NORMAN, we predicted logK OW for over 4,000 compounds, identifying 244 outliers out of 4519. Further categorizing the database into eight Organisation for Economic Co-operation and Development (OECD) categories, we confirmed fluorine atoms role in enhanced bioaccumulation. Utilizing predicted logK OW , water solubility logS W , and vapor pressure logVP values, we calculated additional physicochemical properties that are responsible for the transport and dispersion of PFAS in the environment. Parameters such as Henry's Law (k H ), air-water partition coefficient (K AW ), octanol-air coefficient (K OA ), and soil adsorption coefficient (K OC ) exhibited favorable correlations with literature data (R 2  > 0.66). Our study successfully filled data gaps, contributing to the understanding of ubiquitous PFAS in the environment and estimating missing physicochemical data for these compounds., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

The rapid development of engineered nanomaterials (ENMs) causes humans to become increasingly exposed to them. Therefore, a better understanding of the health impact of ENMs is highly demanded. Considering the 3Rs (Replacement, Reduction, and Refinement) principle, in vitro and computational methods are excellent alternatives for testing on animals. Among computational methods, nano-quantitative structure-activity relationship (nano-QSAR), which links the physicochemical and structural properties of EMNs with biological activities, is one of the leading method. The nature of toxicological experiments has evolved over the last decades; currently, one experiment can provide thousands of measurements of the organism's functioning at the molecular level. At the same time, the capacity of the in vitro systems to mimic the human organism is also improving significantly. Hence, the authors would like to discuss whether the nano-QSAR approach follows modern toxicological studies and takes full advantage of the opportunities offered by modern toxicological platforms. Challenges and possibilities for improving data integration are underlined narratively, including the need for a consensus built between the in vitro and the QSAR domains., (© 2023 Wiley-VCH GmbH.)

Liposomes, nanoscale spherical structures composed of amphiphilic lipids, hold great promise for various pharmaceutical applications, especially as nanocarriers in targeted drug delivery, due to their biocompatibility, biodegradability, and low immunogenicity. Understanding the factors influencing their physicochemical properties is crucial for designing and optimizing liposomes. In this study, we have presented the kernel-weighted local polynomial regression (KwLPR) nano-quantitative structure-property relationships (nano-QSPR) model to predict the zeta potential (ZP) based on the structure of 12 liposome formulations, including 1,2-dioleoyl-sn-glycero-3-phosphoethanolamine (DOPE), 3ß-[N-(N',N'-dimethylaminoethane)-carbamoyl]cholesterol (DC-Chol), 1,2-dioleoyl-3-trimethylammonium-propane (DOTAP), and L-α-phosphatidylcholine (EPC). The developed model is well-fitted ( R 2 = 0.96, RMSE C = 5.76), flexible ( Q CVloo 2 = 0.83, RMSE CVloo = 10.77), and reliable ( Q Ext 2 = 0.89 RMSE Ext = 5.17). Furthermore, we have established the formula for computing molecular nanodescriptors for liposomes, based on constituent lipids' molar fractions. Through the correlation matrix and principal component analysis (PCA), we have identified two key structural features affecting liposomes' zeta potential: hydrophilic-lipophilic balance (HLB) and enthalpy of formation. Lower HLB values, indicating a more lipophilic nature, are associated with a higher zeta potential, and thus stability. Higher enthalpy of formation reflects reduced zeta potential and decreased stability of liposomes. We have demonstrated that the nano-QSPR approach allows for a better understanding of how the composition and molecular structure of liposomes affect their zeta potential, filling a gap in ZP nano-QSPR modeling methodologies for nanomaterials (NMs). The proposed proof-of-concept study is the first step in developing a comprehensive and computationally based system for predicting the physicochemical properties of liposomes as one of the most important drug nano-vehicles., Competing Interests: The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results., (© 2024 Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology.)

The adverse outcome pathway (AOP) framework plays a crucial role in the paradigm shift of tox­icity testing towards the development and use of new approach methodologies. AOPs developed for chemicals are in theory applicable to nanomaterials (NMs). However, only initial efforts have been made to integrate information on NM-induced toxicity into existing AOPs. In a previous study, we identified AOPs in the AOP-Wiki associated with the molecular initiating events (MIEs) and key events (KEs) reported for NMs in scientific literature. In a next step, we analyzed these AOPs and found that mitochondrial toxicity plays a significant role in several of them at the molecular and cellular levels. In this study, we aimed to generate hypothesis-based AOPs related to NM-induced mitochondrial toxicity. This was achieved by integrating knowledge on NM-induced mitochondrial toxicity into all existing AOPs in the AOP-Wiki, which already includes mitochondrial toxicity as a MIE/KE. Several AOPs in the AOP-Wiki related to the lung, liver, cardiovascular and nervous system, with extensively defined KEs and key event relationships (KERs), could be utilized to develop AOPs that are relevant for NMs. However, the majority of the studies included in our literature review were of poor quality, particularly in reporting NM physicochemical characteristics, and NM-relevant mitochondrial MIEs were rarely reported. This study highlights the potential role of NM-induced mitochondrial toxicity in human-relevant adverse outcomes and identifies useful AOPs in the AOP-Wiki for the development of AOPs for NMs.

Assessing research activity is an important step for planning future initiatives oriented toward filling the remaining gaps in a field. Therefore, the objective of the current study was to review recently published research on pulmonary toxicity caused by nanomaterials. However, here, instead of reviewing possible toxic effects and discussing their mode of action, the goal was to establish trends considering for example examined so far nanomaterials or used testing strategies. A total of 2316 related articles retrieved from the three most cited databases (PubMed Scopus, Web of Science), selected based on the title and abstract requirements, were used as the source of the review. Based on the bibliometric analysis, the nano-meter metal oxides, and carbon-based nanotubes were identified as the most frequently studied nanomaterials, while quantum dots, which might induce possible harmful effects, were not considered so far. The majority of testing of pulmonary safety is based on in vitro studies with observed growth of the contribution of novel testing strategies, such as 3D lung model, air-liquid interface system, or omic analysis.

This work aimed to verify whether it is possible to extend the applicability domain (AD) of existing QSPR (Quantitative Structure-Property Relationship) models by employing a strategy involving additional quantum-chemical calculations. We selected two published QSPR models: for water solubility, logS W , and vapor pressure, logVP of PFAS as case studies. We aimed to enlarge set of compounds used to build the model by applying factorial planning to plan the augmentation of the set of these compounds based on their structural features (descriptors). Next, we used the COSMO-RS model to calculate the logS W and logVP for selected chemicals. This allowed filling gaps in the experimental data for further training QSPR models. We improved the published models by significantly extending number of compounds for which theoretical predictions are reliable (i.e., extending the AD). Additionally, we performed external validation that had not been carried out in original models. To test effectiveness of the AD extension, we screened 4519 PFAS from NORMAN Database. The number of compounds outside the domain was reduced comparing the original model for both properties. Our work shows that combining physics-based methods with data-driven models can significantly improve the performance of predictions of phys-chem properties relevant for the chemical risk assessment., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023. Published by Elsevier Ltd.)

Nanoinformatics demands accurate predictive models to assess the potential hazards of nanomaterials (NMs). However, limited data availability and the diverse nature of NMs physicochemical properties and their interaction with biological media, hinder the development of robust nano-Quantitative Structure-Activity Relationship (QSAR) models. This article proposes an approach that combines artificially data generation techniques and topological projections to address the challenges of insufficient dataset sizes and their limited representativeness of the chemical space. By leveraging the rich information embedded in the topological features, this methodology enhances the representation of the chemical space, enabling a more an exploration of the structure-activity relationships. We demonstrate the efficacy of our approach through extensive experiments, employing various machine learning regression algorithms to validate the methodology. Finally, we compare two different resampling approaches based on different modeling scenarios. The results showcase a significant improved predictive performance of QSAR models demonstrating a promising strategy to overcome the limitations of small datasets in the field of nanoinformatics. The proposed approach offers noteworthy potential for advancing nanoinformatics research within the nanosafety domain by enabling the development of more accurate predictive models for assessing the potential hazards associated with NMs.

To control stability in a biological medium, several factors affecting the zeta potential (ζ) of nanoparticles (NPs) must be considered, including complex interactions between the nanostructure and the composition of the protein corona (PC). Effective in silico methods (based on machine learning and quantitative structure-property relationship (QSPR) models) could help predict and characterize the relationship between the physicochemical properties of NP and the formation of PC and biological outcomes in the medium at an early stage of the experiment. However, the models currently developed are limited to simple descriptors that do not represent the complex interactions between the core, the coating, and their PC fingerprints. To be useful, the models developed should be described as a function of both the structural properties determined by the core and coating of the NPs and the biological medium determined by the formation of the protein corona. We have developed a set of complex descriptors that describe the quantitative relationship between the value of the zeta potential (ζ), core, the coating of NPs, and their PC fingerprints (the so-called nano-QSPR model). The nano-QSPR model was developed based on a genetic algorithm using a partial least-squares regression method (GA-PLS), which is characterized by high external predictive power ( Q 2 EXT = 0.89). The GA-PLS model was developed using descriptors that describe (i) the core structure (determined by 7 different types of polymer-based NMs in the range of 20 different sizes), (ii) the coating structure with 7 different functional groups, and (iii) 80 different types of protein compositions adsorbed on the surface of the NPs. The presented study answers the question of how complex interactions between the corona and NP determine the zeta potential (ζ) of NP in a given medium. Moreover, our current study is a proof-of-concept that the zeta potential of NPs modeled on the original structure depends not only on the NPs themselves but also on the structure and properties determined by the NP core and coating, as well as the biological medium determined by the formation of the protein corona. On the basis of these results, our studies will be useful in determining the stability and mechanism of cell uptake, toxicity, and ability to predict the zeta potential of compounds not yet tested.

The lack of knowledge about the uptake of NPs by biological cells poses a significant problem for drug delivery. For this reason, designing an appropriate model is the main challenge for modelers. To address this problem, molecular modeling studies that can describe the mechanism of cellular uptake of drug-loaded nanoparticles have been conducted in recent decades. In this context, we developed three different models for the amphipathic nature of drug-loaded nanoparticles (MTX-SS-γ-PGA), whose cellular uptake mechanism was predicted by molecular dynamics studies. Many factors affect nanoparticle uptake, including nanoparticle physicochemical properties, protein-particle interactions, and subsequent agglomeration, diffusion, and sedimentation. Therefore, the scientific community needs to understand how these factors can be controlled and the NP uptake of nanoparticles. Based on these considerations, in this study, we investigated for the first time the effects of the selected physicochemical properties of the anticancer drug methotrexate (MTX) grafted with hydrophilic-γ-polyglutamic acid (MTX-SS-γ-PGA) on its cellular uptake at different pH values. To answer this question, we developed three theoretical models describing drug-loaded nanoparticles (MTX-SS-γ-PGA) at three different pH values, such as (1) pH 7.0 (the so-called neutral pH model), (2) pH 6.4 (the so-called tumor pH model), and (3) pH 2.0 (the so-called stomach pH model). Exceptionally, the electron density profile shows that the tumor model interacts more strongly with the head groups of the lipid bilayer than the other models due to charge fluctuations. Hydrogen bonding and RDF analyses provide information about the solution of the NPs with water and their interaction with the lipid bilayer. Finally, dipole moment and HOMO-LUMO analysis showed the free energy of the solution in the water phase and chemical reactivity, which are particularly useful for determining the cellular uptake of the NPs. The proposed study provides fundamental insights into molecular dynamics (MD) that will allow researchers to determine the influence of pH, structure, charge, and energetics of NPs on the cellular uptake of anticancer drugs. We believe that our current study will be useful in developing a new model for drug delivery to cancer cells with a much more efficient and less time-consuming model.

In this study, we investigated PFAS (per- and polyfluoroalkyl substances) binding potencies to nuclear hormone receptors (NHRs): peroxisome proliferator-activated receptors (PPARs) α, β, and γ and thyroid hormone receptors (TRs) α and β. We have simulated the docking scores of 43 perfluoroalkyl compounds and based on these data developed QSAR (Quantitative Structure-Activity Relationship) models for predicting the binding probability to five receptors. In the next step, we implemented the developed QSAR models for the screening approach of a large group of compounds (4464) from the NORMAN Database. The in silico analyses indicated that the probability of PFAS binding to the receptors depends on the chain length, the number of fluorine atoms, and the number of branches in the molecule. According to the findings, the considered PFAS group bind to the PPARα, β, and γ only with low or moderate probability, while in the case of TR α and β it is similar except that those chemicals with longer chains show a moderately high probability of binding.

Natural and engineered nanoparticles (NPs) entering the environment are influenced by many physicochemical processes and show various behavior in different systems (e.g., natural waters showing different characteristics). Determining the physicochemical characteristics and predicting the behavior of nanoparticles ending up in the natural aquatic environment are key aspects of their risk assessment. Here, we show that the quantitative structure-property relationship modeling method used in nanoinformatics (nano-QSPR) can be successfully applied to predict environmental fate-relevant properties (electrophoretic mobility) of TiO 2 , ZnO, and CeO 2 nanoparticles. However, in contrast to the previous works, we postulate to use, in parallel: (i) the nanoparticles' structure descriptors (S-descriptors) and (ii) the environment descriptors (E-descriptors) as the input variables. Thus, the method should be abbreviated more precisely as nano-QSEPR ("E" stands for the "environment"). As a proof-of-the-concept, we have developed a group of models (including MLR, GA-PLS, PCR, and Meta-Consensus models) with high predictive capabilities (Q EXT 2 = 0.931 for the GA-PLS model), where the S-descriptors are represented by the core-shell model descriptor and the E-descriptors - by different ambient water features (including ions concentration and the ionic strength). The newly proposed nano-QSEPR modeling scheme can be efficiently used to design safe and sustainable nanomaterials., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022. Published by Elsevier B.V.)

Engineered nanomaterials (ENMs) enable new and enhanced products and devices in which matter can be controlled at a near-atomic scale (in the range of 1 to 100 nm). However, the unique nanoscale properties that make ENMs attractive may result in as yet poorly known risks to human health and the environment. Thus, new ENMs should be designed in line with the idea of safe-and-sustainable-by-design (SSbD). The biological activity of ENMs is closely related to their physicochemical characteristics, changes in these characteristics may therefore cause changes in the ENMs activity. In this sense, a set of physicochemical characteristics (for example, chemical composition, crystal structure, size, shape, surface structure) creates a unique 'representation' of a given ENM. The usability of these characteristics or nanomaterial descriptors (nanodescriptors) in nanoinformatics methods such as quantitative structure-activity/property relationship (QSAR/QSPR) models, provides exciting opportunities to optimize ENMs at the design stage by improving their functionality and minimizing unforeseen health/environmental hazards. A computational screening of possible versions of novel ENMs would return optimal nanostructures and manage ('design out') hazardous features at the earliest possible manufacturing step. Safe adoption of ENMs on a vast scale will depend on the successful integration of the entire bulk of nanodescriptors extracted experimentally with data from theoretical and computational models. This Review discusses directions for developing appropriate nanomaterial representations and related nanodescriptors to enhance the reliability of computational modelling utilized in designing safer and more sustainable ENMs., (© 2022. Springer Nature Limited.)

This work aimed to investigate the interaction of bovine serum albumin with newly synthesized potent new pyrene derivatives (PS1 and PS2), which might prove useful to have a better antibacterial character as found for similar compounds in the previous report [Low et al. Bioconjugate Chemistry 2014, 12, 2269-2284]. However, to date, binding studies with plasma protein are still unknown. Steady-state fluorescence spectroscopy and lifetime fluorescence studies show that the static interaction binding mode and binding constants of PS1 and PS2 are 7.39 and 7.81 [ K b × 10 5 (M -1 )], respectively. The experimental results suggest that hydrophobic forces play a crucial role in interacting pyrene derivatives with BSA protein. To verify this, molecular docking and molecular dynamics simulations were performed to predict the nature of the interaction and the dynamic behavior of the two compounds in the BSA complex, PS1 and PS2, under physiological conditions of pH = 7.1. In addition, the free energies of binding for the BSA-PS1 and BSA-PS2 complexes were estimated at 300 K based on the molecular mechanics of the Poisson-Boltzmann surface (MMPBSA) with the Gromacs package. PS2 was found to have a higher binding affinity than PS1. To determine the behavior of the orbital transitions in the ground state geometry, we found that both compounds have similar orbital transitions from HOMO-LUMO via π → π* and HOMO-1-LUMO+1 via n → π*, which was included in the FMO analysis. A cytotoxicity study was performed to determine the toxicity of the compounds. Based on the MD study, the stability of the compounds with BSA and the dynamic binding modes were further revealed, as well as the nature of the binding force components involved and the important residues involved in the binding process. From the binding energy analysis, it can be assumed that PS2 may be more active than PS1.

Exposure to hexavalent chromium Cr(VI) may occur in several occupational activities, placing workers in many industries at risk for potential related health outcomes. Untargeted metabolomics was applied to investigate changes in metabolic pathways in response to Cr(VI) exposure. We obtained our data from a study population of 220 male workers with exposure to Cr(VI) and 102 male controls from Belgium, Finland, Poland, Portugal and the Netherlands within the HBM4EU Chromates Study. Urinary metabolite profiles were determined using liquid chromatography mass spectrometry, and differences between post-shift exposed workers and controls were analyzed using principal component analysis. Based on the first two principal components, we observed clustering by industrial chromate application, such as welding, chrome plating, and surface treatment, distinct from controls and not explained by smoking status or alcohol use. The changes in the abundancy of excreted metabolites observed in workers reflect fatty acid and monoamine neurotransmitter metabolism, oxidative modifications of amino acid residues, the excessive formation of abnormal amino acid metabolites and changes in steroid and thyrotropin-releasing hormones. The observed responses could also have resulted from work-related factors other than Cr(VI). Further targeted metabolomics studies are needed to better understand the observed modifications and further explore the suitability of urinary metabolites as early indicators of adverse effects associated with exposure to Cr(VI).

Nano-QSAR models can be effectively used for prediction of the biological activity of nanomaterials that have not been experimentally tested before. However, their use is associated with the need to have appropriate knowledge and skills in chemoinformatics. Thus, they are mainly aimed at specialists in the field. This significantly limits the potential group of recipients of the developed solutions. In this perspective, the purpose of the presented research was to develop an easily accessible and user-friendly web-based application that could enable the prediction of TiO 2 -based multicomponent nanomaterials cytotoxicity toward Chinese Hamster Ovary (CHO-K1) cells. The graphical user interface is clear and intuitive and the only information required from the user is the type and concentration of the metals which will be modifying TiO 2 -based nanomaterial. Thanks to this, the application will be easy to use not only by cheminformatics but also by specialists in the field of nanotechnology or toxicology, who will be able to quickly predict cytotoxicity of desired nanoclusters. We have performed case studies to demonstrate the features and utilities of developed application. The NanoMixHamster application is freely available at https://nanomixhamster.cloud.nanosolveit.eu/.

Nano-QSAR model allows for prediction of the toxicity of materials that have not been experimentally tested before by linking the nano-related structural properties with the biological responses induced by nanomaterials. Prediction of adverse effects caused by substances without having to perform time- and cost-consuming experiments makes QSAR models promising tools for supporting risk assessment. However, very often, newly developed nano-QSAR models are not used in practice due to the complexity of their algorithms, the necessity to have experience in chemoinformatics, and their poor accessibility. In this perspective, the aim of this paper is to encourage developers of the QSAR models to take the effort to prepare user-friendly applications based on predictive models. This would make the developed models accessible to a wider community, and, in effect, promote their further application by regulators and decision-makers. Here, we describe a web-based application that enables to predict the transcriptomic pathway-level response perturbated in the lungs of mice exposed to multiwalled carbon nanotubes. The developed application is freely available at http://aop173-event1.nanoqsar-aop.com/apps/aop_app. It requires only two types of input information related to analyzed nanotubes (their length and diameter) to assess the doses that initiate the inflammation process that may lead to lung fibrosis.

In this work, we have employed an intramolecular charge transfer-based DMN colorimetric probe for the rapid naked-eye detection of cyanide ions in solution as well as real water samples. The intermolecular interaction between the DMN probe and cyanide ions in solution was investigated using a combination of spectroscopic and computational methods in this study. The DMN probe exhibited a selective colorimetric response for cyanide ions over the other anions exposed. The cyanide sensing mechanism of the probe has been investigated by 1 H NMR titration and density functional theory calculations. The results reveal that the colorimetric response of the DMN probe is due to the Michael adduct formation in the β-conjugated position of the dicyanovinyl group with cyanide, which blocks intramolecular charge transfer transition. Under optimized experimental conditions, the DMN probe showed a linear plot in the concentration range of 0.01-0.25 μM, with a detection limit of 23 nM. Further, a 3D printed portable accessory for the smartphone and an open-source android application is developed to suit the DMN probe for on-site work. In addition, we have developed the microfluidic paper-based analytical device that could selectively detect cyanide ions at very low concentration using a colorimetric DMN probe. In addition, the DMN probe was effectively used to determine the cyanide ion in a variety of water samples.

It is acknowledged that the physicochemical properties of nanomaterials (NMs) have an impact on their toxicity and, eventually, their pathogenicity. These properties may include the NMs' surface chemical composition, size, shape, surface charge, surface area, and surface coating with ligands (which can carry different functional groups as well as proteins). Nanotopography, defined as the specific surface features at the nanoscopic scale, is not widely acknowledged as an important physicochemical property. It is known that the size and shape of NMs determine their nanotopography which, in turn, determines their surface area and their active sites. Nanotopography may also influence the extent of dissolution of NMs and their ability to adsorb atoms and molecules such as proteins. Consequently, the surface atoms (due to their nanotopography) can influence the orientation of proteins as well as their denaturation. However, although it is of great importance, the role of surface topography (nanotopography) in nanotoxicity is not much considered. Many of the issues that relate to nanotopography have much in common with the fundamental principles underlying classic catalysis. Although these were developed over many decades, there have been recent important and remarkable improvements in the development and study of catalysts. These have been brought about by new techniques that have allowed for study at the nanoscopic scale. Furthermore, the issue of quantum confinement by nanosized particles is now seen as an important issue in studying nanoparticles (NPs). In catalysis, the manipulation of a surface to create active surface sites that enhance interactions with external molecules and atoms has much in common with the interaction of NP surfaces with proteins, viruses, and bacteria with the same active surface sites of NMs. By reviewing the role that surface nanotopography plays in defining many of the NMs' surface properties, it reveals the need for its consideration as an important physicochemical property in descriptive and predictive toxicology. Through the manipulation of surface topography, and by using principles developed in catalysis, it may also be possible to make safe-by-design NMs with a reduction of the surface properties which contribute to their toxicity.

Nanotechnology is a key enabling technology with billions of euros in global investment from public funding, which include large collaborative projects that have investigated environmental and health safety aspects of nanomaterials, but the reuse of accumulated data is clearly lagging behind. Here we summarize challenges and provide recommendations for the efficient reuse of nanosafety data, in line with the recently established FAIR (findable, accessible, interoperable and reusable) guiding principles. We describe the FAIR-aligned Nanosafety Data Interface, with an aggregated findability, accessibility and interoperability across physicochemical, bio-nano interaction, human toxicity, omics, ecotoxicological and exposure data. Overall, we illustrate a much-needed path towards standards for the optimized use of existing data, which avoids duplication of efforts, and provides a multitude of options to promote safe and sustainable nanotechnology.

This study presents a novel strategy that employs quantitative structure-activity relationship models for nanomaterials (Nano-QSAR) for predicting transcriptomic pathway level response using lung tissue inflammation, an essential key event (KEs) in the existing adverse outcome pathway (AOP) for lung fibrosis, as a model response. Transcriptomic profiles of mouse lungs exposed to ten different multiwalled carbon nanotubes (MWCNTs) are analyzed using statistical and bioinformatics tools. Three pathways "agranulocyte adhesion and diapedesis," "granulocyte adhesion and diapedesis," and "acute phase signaling," that (1) are commonly perturbed across the MWCNTs panel, (2) show dose response (Benchmark dose, BMDs), and (3) are anchored to the KEs identified in the lung fibrosis AOP, are considered in modelling. The three pathways are associated with tissue inflammation. The results show that the aspect ratio (κ) of MWCNTs is directly correlated with the pathway BMDs. The study establishes a methodology for QSAR construction based on canonical pathways and proposes a MWCNTs grouping strategy based on the κ-values of the specific pathway associated genes. Finally, the study shows how the AOP framework can help guide QSAR modelling efforts; conversely, the outcome of the QSAR modelling can aid in refining certain aspects of the AOP in question (here, lung fibrosis)., (© 2021 Wiley-VCH GmbH.)

Zeta potential is usually measured to estimate the surface charge and the stability of nanomaterials, as changes in these characteristics directly influence the biological activity of a given nanoparticle. Nowadays, theoretical methods are commonly used for a pre-screening safety assessments of nanomaterials. At the same time, the consistency of data on zeta potential measurements in the context of environmental impact is an important challenge. The inconsistency of data measurements leads to inaccuracies in predictive modeling. In this article, we report a new curated dataset of zeta potentials measured for 208 silica- and metal oxide nanoparticles in different media. We discuss the data curation framework for zeta potentials designed to assess the quality and usefulness of the literature data for further computational modeling. We also provide an analysis of specific trends for the datapoints harvested from different literature sources. In addition to that, we present for the first time a structure-property relationship model for nanoparticles (nano-SPR) that predicts values of zeta potential values measured in different environmental conditions (i.e., biological media and pH)., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Manufactured nanomaterials (NMs) are increasingly used in a wide range of industrial applications leading to a constant increase in the market size of nano-enabled products. The increased production and use of NMs are raising concerns among different stakeholder groups with regard to their effects on human and environmental health. Currently, nanosafety hazard assessment is still widely performed using in vivo (animal) models, however the development of robust and reg­ulatory relevant strategies is required to prioritize and/or reduce animal testing. An adverse outcome pathway (AOP) is a structured representation of biological events that start from a molecular initiating event (MIE) leading to an adverse outcome (AO) through a series of key events (KEs). The AOP framework offers great advancement to risk assessment and regulatory safety assessments. While AOPs for chemicals have been more frequently reported, the AOP collection for NMs is limited. By using existing AOPs, we aimed to generate simple and testable strategies to predict if a given NM has the potential to induce a MIE leading to an AO through a series of KEs. Firstly, we identified potential MIEs or initial KEs reported for NMs in the literature. Then, we searched the identified MIE or initial KEs as keywords in the AOP-Wiki to find associated AOPs. Finally, using two case studies, we demonstrate how in vitro strategies can be used to test the identified MIE/KEs.

Chemoinformatics has developed efficient ways of representing chemical structures for small molecules as simple text strings, simplified molecular-input line-entry system (SMILES) and the IUPAC International Chemical Identifier (InChI), which are machine-readable. In particular, InChIs have been extended to encode formalized representations of mixtures and reactions, and work is ongoing to represent polymers and other macromolecules in this way. The next frontier is encoding the multi-component structures of nanomaterials (NMs) in a machine-readable format to enable linking of datasets for nanoinformatics and regulatory applications. A workshop organized by the H2020 research infrastructure NanoCommons and the nanoinformatics project NanoSolveIT analyzed issues involved in developing an InChI for NMs ( NInChI ). The layers needed to capture NM structures include but are not limited to: core composition (possibly multi-layered); surface topography; surface coatings or functionalization; doping with other chemicals; and representation of impurities. NM distributions (size, shape, composition, surface properties, etc.), types of chemical linkages connecting surface functionalization and coating molecules to the core, and various crystallographic forms exhibited by NMs also need to be considered. Six case studies were conducted to elucidate requirements for unambiguous description of NMs. The suggested NInChI layers are intended to stimulate further analysis that will lead to the first version of a "nano" extension to the InChI standard.

A significant number of experimental studies are supported by computational methods such as quantitative structure-activity relationship modeling of nanoparticles (Nano-QSAR). This is especially so in research focused on design and synthesis of new, safer nanomaterials using safe-by-design concepts. However, Nano-QSAR has a number of important limitations. For example, it is not clear which descriptors that describe the nanoparticle physicochemical and structural properties are essential and can be adjusted to alter the target properties. This limitation can be overcome with the use of the Structure-Activity Prediction Network (SAPNet) presented in this paper. There are three main phases of building the SAPNet. First, information about the structural characterization of a nanomaterial, its physical and chemical properties and toxicity is compiled. Then, the most relevant properties (intrinsic/extrinsic) likely to influence the ENM toxicity are identified by developing "meta-models". Finally, these "meta-models" describing the dependencies between the most relevant properties of the ENMs and their adverse biological properties are developed. In this way, the network is built layer by layer from the endpoint (e.g. toxicity or other properties of interest) to descriptors that describe the particle structure. Therefore, SAPNets go beyond the current standards and provide sufficient information on what structural features should be altered to obtain a material with desired properties.

Nanotechnologies have reached maturity and market penetration that require nano-specific changes in legislation and harmonization among legislation domains, such as the amendments to REACH for nanomaterials (NMs) which came into force in 2020. Thus, an assessment of the components and regulatory boundaries of NMs risk governance is timely, alongside related methods and tools, as part of the global efforts to optimise nanosafety and integrate it into product design processes, via Safe(r)-by-Design (SbD) concepts. This paper provides an overview of the state-of-the-art regarding risk governance of NMs and lays out the theoretical basis for the development and implementation of an effective, trustworthy and transparent risk governance framework for NMs. The proposed framework enables continuous integration of the evolving state of the science, leverages best practice from contiguous disciplines and facilitates responsive re-thinking of nanosafety governance to meet future needs. To achieve and operationalise such framework, a science-based Risk Governance Council (RGC) for NMs is being developed. The framework will provide a toolkit for independent NMs' risk governance and integrates needs and views of stakeholders. An extension of this framework to relevant advanced materials and emerging technologies is also envisaged, in view of future foundations of risk research in Europe and globally., (© 2020 The Authors. Published by WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Ionic liquids (ILs) containing distinct nitrogen-bearing organic cations (pyridinium, pyrrolidinium, imidazolium, ammonium, morpholinium) were first used for the preparation of 23 IL-TiO 2 types of composites by ionic liquid assisted solvothermal synthesis. These 23 optimal ILs structures (i.e. compounds exhibiting an optimal combination of specific properties, functionality, and safety) for synthesis and experimental validation were selected by computational high-throughput screening from a combinatorically created library containing 836 ILs theoretically designed and characterized candidates. Then, selected IL-TiO 2 structures with potential photocatalytic activity were synthesized with the use of solvothermal reaction. Then, the decomposition level, the role of the individual IL cation structure on the morphology, thermal stability, surface and photocatalytic properties of the IL-TiO 2 microparticles were determined experimentally. The chemoinformatic analysis of the relationship between the structure of the ionic liquid, its thermal stability under the conditions of synthesis and photocatalytic activity was applied for the first time. The results presented here are the first step in the development of methodology (combined experimental and theoretical) that may simplify the procedure of designing safer and more efficient TiO 2 -based photocatalyst. The developed computational methodology makes it possible to predict properties of newly synthesized IL-TiO 2 materials before synthesis and identifies structural features of ILs that influence the efficiency of IL-TiO 2 system. The presented approach reduces the number and cost of necessary experiments, as well as increases the success ratio of efficient TiO 2 -based photocatalyst design by a selection of optimal IL structures (i.e. ionic liquid characterized by a combination of most promising physicochemical features)., Competing Interests: Conflict of interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier Inc. All rights reserved.)

The process of encoding the structure of chemicals by molecular descriptors is a crucial step in quantitative structure-activity/property relationships (QSAR/QSPR) modeling. Since ionic liquids (ILs) are disconnected structures, various ways of representing their structure are used in the QSAR studies: the models can be based on descriptors either derived for particular ions or for the whole ionic pair. We have examined the influence of the type of IL representation (separate ions vs. ionic pairs) on the model's quality, the process of the automated descriptors selection and reliability of the applicability domain (AD) assessment. The result of the benchmark study showed that a less precise description of ionic liquid, based on the 2D descriptors calculated for ionic pairs, is sufficient to develop a reliable QSAR/QSPR model with the highest accuracy in terms of calibration as well as validation. Moreover, the process of a descriptors' selection is more effective when the possible number of variables can be decreased at the beginning of model development. Additionally, 2D descriptors usually demand less effort in mechanistic interpretation and are more convenient for virtual screening studies.

Preprocessing of transcriptomics data plays a pivotal role in the development of toxicogenomics-driven tools for chemical toxicity assessment. The generation and exploitation of large volumes of molecular profiles, following an appropriate experimental design, allows the employment of toxicogenomics (TGx) approaches for a thorough characterisation of the mechanism of action (MOA) of different compounds. To date, a plethora of data preprocessing methodologies have been suggested. However, in most cases, building the optimal analytical workflow is not straightforward. A careful selection of the right tools must be carried out, since it will affect the downstream analyses and modelling approaches. Transcriptomics data preprocessing spans across multiple steps such as quality check, filtering, normalization, batch effect detection and correction. Currently, there is a lack of standard guidelines for data preprocessing in the TGx field. Defining the optimal tools and procedures to be employed in the transcriptomics data preprocessing will lead to the generation of homogeneous and unbiased data, allowing the development of more reliable, robust and accurate predictive models. In this review, we outline methods for the preprocessing of three main transcriptomic technologies including microarray, bulk RNA-Sequencing (RNA-Seq), and single cell RNA-Sequencing (scRNA-Seq). Moreover, we discuss the most common methods for the identification of differentially expressed genes and to perform a functional enrichment analysis. This review is the second part of a three-article series on Transcriptomics in Toxicogenomics.

The starting point of successful hazard assessment is the generation of unbiased and trustworthy data. Conventional toxicity testing deals with extensive observations of phenotypic endpoints in vivo and complementing in vitro models. The increasing development of novel materials and chemical compounds dictates the need for a better understanding of the molecular changes occurring in exposed biological systems. Transcriptomics enables the exploration of organisms' responses to environmental, chemical, and physical agents by observing the molecular alterations in more detail. Toxicogenomics integrates classical toxicology with omics assays, thus allowing the characterization of the mechanism of action (MOA) of chemical compounds, novel small molecules, and engineered nanomaterials (ENMs). Lack of standardization in data generation and analysis currently hampers the full exploitation of toxicogenomics-based evidence in risk assessment. To fill this gap, TGx methods need to take into account appropriate experimental design and possible pitfalls in the transcriptomic analyses as well as data generation and sharing that adhere to the FAIR (Findable, Accessible, Interoperable, and Reusable) principles. In this review, we summarize the recent advancements in the design and analysis of DNA microarray, RNA sequencing (RNA-Seq), and single-cell RNA-Seq (scRNA-Seq) data. We provide guidelines on exposure time, dose and complex endpoint selection, sample quality considerations and sample randomization. Furthermore, we summarize publicly available data resources and highlight applications of TGx data to understand and predict chemical toxicity potential. Additionally, we discuss the efforts to implement TGx into regulatory decision making to promote alternative methods for risk assessment and to support the 3R (reduction, refinement, and replacement) concept. This review is the first part of a three-article series on Transcriptomics in Toxicogenomics. These initial considerations on Experimental Design, Technologies, Publicly Available Data, Regulatory Aspects, are the starting point for further rigorous and reliable data preprocessing and modeling, described in the second and third part of the review series.

Transcriptomics data are relevant to address a number of challenges in Toxicogenomics (TGx). After careful planning of exposure conditions and data preprocessing, the TGx data can be used in predictive toxicology, where more advanced modelling techniques are applied. The large volume of molecular profiles produced by omics-based technologies allows the development and application of artificial intelligence (AI) methods in TGx. Indeed, the publicly available omics datasets are constantly increasing together with a plethora of different methods that are made available to facilitate their analysis, interpretation and the generation of accurate and stable predictive models. In this review, we present the state-of-the-art of data modelling applied to transcriptomics data in TGx. We show how the benchmark dose (BMD) analysis can be applied to TGx data. We review read across and adverse outcome pathways (AOP) modelling methodologies. We discuss how network-based approaches can be successfully employed to clarify the mechanism of action (MOA) or specific biomarkers of exposure. We also describe the main AI methodologies applied to TGx data to create predictive classification and regression models and we address current challenges. Finally, we present a short description of deep learning (DL) and data integration methodologies applied in these contexts. Modelling of TGx data represents a valuable tool for more accurate chemical safety assessment. This review is the third part of a three-article series on Transcriptomics in Toxicogenomics.

Nanotechnology has enabled the discovery of a multitude of novel materials exhibiting unique physicochemical (PChem) properties compared to their bulk analogues. These properties have led to a rapidly increasing range of commercial applications; this, however, may come at a cost, if an association to long-term health and environmental risks is discovered or even just perceived. Many nanomaterials (NMs) have not yet had their potential adverse biological effects fully assessed, due to costs and time constraints associated with the experimental assessment, frequently involving animals. Here, the available NM libraries are analyzed for their suitability for integration with novel nanoinformatics approaches and for the development of NM specific Integrated Approaches to Testing and Assessment (IATA) for human and environmental risk assessment, all within the NanoSolveIT cloud-platform. These established and well-characterized NM libraries (e.g. NanoMILE, NanoSolutions, NANoREG, NanoFASE, caLIBRAte, NanoTEST and the Nanomaterial Registry (>2000 NMs)) contain physicochemical characterization data as well as data for several relevant biological endpoints, assessed in part using harmonized Organisation for Economic Co-operation and Development (OECD) methods and test guidelines. Integration of such extensive NM information sources with the latest nanoinformatics methods will allow NanoSolveIT to model the relationships between NM structure (morphology), properties and their adverse effects and to predict the effects of other NMs for which less data is available. The project specifically addresses the needs of regulatory agencies and industry to effectively and rapidly evaluate the exposure, NM hazard and risk from nanomaterials and nano-enabled products, enabling implementation of computational 'safe-by-design' approaches to facilitate NM commercialization., (© 2020 The Authors.)

The acidity of Lewis-Brønsted superacids can be derived from the theoretical calculations as the Gibbs free energy of the deprotonation reaction (ΔG acid ), which describes the tendency of a studied compound to donate a proton. This paper presents the first Quantitative Structure - Property Relationship (QSPR) model that correlates the ΔG acid of superacid (HF/MeX 3 formula (X=F, Cl, Br)) with their structure. Developed model is well fitted, roubustness, has good predictive abilities, fulfills all OECD recommendation for good model. Obtained results provide the insight into the relation of structural features of superacids, which are responsible for their acid strength - the structures characterized by strong F-Me dative bond (with relatively large vibrational frequency), small positive partial atomic charge on Me central atom, possibly large polarity exhibit large acid strength. Such assumption can be used in the future as valuable information in the process of the designing new, stronger, more effective superacids., (© 2019 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.)